A direct numerical simulation (DNS) of a planar jet with a second-order chemical reaction is performed using four different Schmidt number (Sc = 1, 2, 4, 8). Reactant A is contained in the jet flow and reactant B is contained in the ambient flow. The chemical reaction ( A + B → P ) proceeds by molecular mixing of the two reactants in the jet. The results show that the higher Sc becomes, the finer the structure of the product concentration becomes. The region where the reaction occurs changes depending on the Sc. Mean concentration and mean production rate conditioned on the distance from the T/NT (Turbulent/Non-Turbulent) interface is calculeted. The change in the conditional mean concentration of the reactive species near the T/NT interface occurs within the order of Taylor’s microscale regardless of the difference in Sc. In the chemical reaction field, the slope of the concentration change at T/NT does not differ much depending on the Sc. When the Sc is high, the mean production rate decreases. This is because the molecular diffusion is weak and small-scale concentration fluctuation occurs at high Sc. It also found that the thickness of the reaction layer becomes thinner as the Sc increases and the thickness of the reaction layer is scaled on the Bachelor scale ηB.
To investigate the effect of Reynolds number on high Schmidt number scalar statistics, we measured the dye concentration fluctuation in liquid phase turbulent axisymmetric jet for various Reynolds numbers. The dye concentration fluctuations were measured by LIF method using the optical fiber probe with a high spatial resolution of 2.5 μm, which is equivalent to the minimum scale of concentration fluctuations. It was found that the concentration fluctuation power spectrum shows the -1 power law in the high wavenumber region regardless of Reynolds number. It was also shown that the relative concentration fluctuation intensity on the jet central axis does not depend on Reynolds numbers at high Reynolds numbers.
The heterogeneous distribution of contaminant in enclosed environment is essentially formed by non-uniformity of airflow pattern and maldistribution of contaminant generation source. Recent technologies to analyze flow and contaminant distributions in indoor environment can allow the detail prediction of concentration distributions. However, in order to understand the essential forming structure of heterogeneity of contaminant concentration, quantitative and qualitative indices that can be explained “Why did this heterogeneous concentration distribution and the concentration value at a point form?” is required. Against this motivation, we have proposed new index, Net Escape Velocity (NEV), for evaluating ventilation efficiency at a point under heterogeneous concentration distribution of contaminant in indoor environment and reported/demonstrated the fundamental concept and application example for ventilation design. In this study, in develop NEV concept into practical ventilation design, we firstly try to apply NEV concept into contaminant concentration field formation in the presence of chemical reaction in indoor environment. Secondary, we also discuss the impact of turbulent diffusivity, i.e. Turbulent Schmidt number as basically a function of physical properties of target chemical compounds, on the estimation results of NEV.
In this paper, we focused on the influence of the Turbulent Schmidt number and the first Damköhler number on a contaminant concentration field analysis result, the diffusion field analyses were performed by gradually changing the Turbulent Schmidt number and the first Damköhler number, and the ventilation efficiency indices NEV and Net Escape Probability (NEP) proposed us were calculated using the results of airflow field and diffusion field, and try to consider the concentration field formation mechanism.
Sensitivity analysis was performed by sequentially changing the Turbulent Schmidt number and the first Damköhler number as the model parameters for a simple two-dimensional (2D) flow field. The size of the simple room model is 10L0×10L0 (L0 is the representative length scale and corresponds to the size of the inlet opening) with a non-dimensional scale. Diffusion field analysis of contaminants (scalars) was performed by gradually changing the Turbulent Schmidt number σt in the range of 0.5 to 1.0 and the first Damköhler number Da in the range of 1× 10-5 to 1× 10-2. In addition, we report the result of evaluating the ventilation efficiency indices NEV and NEP by the result of diffusion field analysis. The NEV/ NEP value at a CV is calculated using the result of diffusion field, which is a contaminant, is only generated in the CV, and NEV/ NEP distribution is obtained by synthesizing the NEV/ NEP value of each CV. It means that 100 calculations should be carried out, which corresponds to the total numbers of meshes for NEV/ NEP distribution.
As a result, focusing on the analysis result of the ventilation index NEV, when the Turbulent Schmidt number σt is greatly changed from 0.5 to 1.0, a clear difference cannot be confirmed in the entire NEV distribution, but the value of NEV at the central CV in the room is increased 44.2%, and indicating contribution of contaminant discharge efficiency due to turbulent diffusion in addition to convection wind speed. When the first Damköhler number Da was changed from 1.0× 10-5 to 1.0× 10-2, there was no change in the overall NEV distribution, and no significant difference was confirmed on each CV value. Compared with the convection and diffusion effect in the room, the decontamination effect by the reaction accompanying the spraying of the chemical substance in the room air is very small.
Numerical simulation of impact of particles on a tubular wall in turbulent air flow has been conducted. It is found that the impact velocity normalized by the friction velocity decreased with the particle- Schmidt number. However, for larger Shmidt number, the impact velocity could not be expressed by the Schmidt number because the motion of particle depends not only on the turbulent diffusion but also on the inertia. The total impact velocity, Vtotal, increases with the air velocity, u0, and can be expressed by Vtotal = k u01.1. The result agrees qualitatively with Masda’s experimental data of the impact charge. The distribution of the total impact velocity agrees qualitatively with the experimental charge distribution.